Brain Computer Interfaces (BCI) have gained increased attention over the last decades because they offer intuitive control in a plethora of applications where other interfaces (e.g. joysticks) are inadequate or impractical. Moreover, these kinds of interfaces allow people with motor disabilities due to amyotrophic lateral sclerosis (ALS), spinal cord injuries (SCI) etc. to interact with the world. This interaction is found in many forms and can vary between controlling the motion of a cursor on a screen to interacting with an actual robotic platform. However, most of the existing systems allow only binary control, while the number of degrees of freedom directly controlled through those interfaces is quite limited, i.e. only one or two in most cases.
There are many types of BCIs and each one of them exploits different functions of the human brain. Most of them rely on the analysis of ElectroEncephaloGraphic (EEG) signals and their features, such as: P300 Event Related Potentials (ERP), Slow Cortical Potentials (SCP), Steady State Visual Evoked Potentials (SSVEP) or Event Related Desynchronization/Synchronization (ERD/ERS) phenomena.
One very popular type of brain-machine interaction is the control of a cursor's 2D position on a screen. This involves the control of brain activity at specific frequency bands chosen appropriately for each user. The advantage of this approach is that the output of the BCIs comprises continuous variables that affect the velocity of the cursor at different directions, thus controlling multiple degrees of freedom (DOFs) at once. However, this approach involves lengthy training sessions until the users can achieve an acceptable level of control.
Many researchers have successfully applied methodologies that output discrete decisions for controlling a robotic platform. These decisions represent the high-level planner for the robot, while the on-board sensors take care of the low-level control, such as obstacle avoidance. One such example is where the user transitions from one brain state to another in order to move a mobile robot through a maze-like environment that avoids collisions based on its onboard sensing. Another example is the use of SSVEP in order to specify objects of interest for the subject and control a robot to move these objects from one place to another as specified by the user. Many of those methodologies rely on different machine learning techniques in order to properly differentiate between different brain states and, thus, correctly identify the users' intent. The advantage of such an approach is that it leads to the extraction of robust features that can be automatically detected for each subject without requiring lengthy training sessions. This results in systems that are highly customizable, more robust and easier to use. However, this comes at the expense of less DOFs because there are certain restrictions on the amount of brain states that such algorithms can detect and discriminate.
In order to address the issues of the previous approaches, many researchers have proposed various hybrid BCI systems. There are two categories of such systems. The first one refers to methods that combine simultaneously or sequentially two different types of EEG signals, as for example where ERD/ERS based signals are combined with P300 potentials. The second category involves systems that combine EEG signals with different types of biosignals such as electromyograms (EMG), electrooculo-grams (EOG), or with assistive technologies (AT), such as wheelchairs, mice or keyboards, i.e. non-biological signals. The goal of both of such systems is either to enhance the accuracy of the brain state classification or provide a type of “brain switch” that can help users complete more complicated tasks. The problem with such systems, especially when multiple EEG modalities are involved, is that there is high complexity, due to the fact that the user has to switch among different interfaces, while in some cases considerable cross-talk across multiple interfaces makes the robustness of the systems questionable.
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